Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909996302729216 |
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| author | Zhang, Lingyu Xu, Pengfei Ban, Rui Zhang, Zhenchao Liu, Songtao Wang, Yan Wang, Yunhai |
| author_facet | Zhang, Lingyu Xu, Pengfei Ban, Rui Zhang, Zhenchao Liu, Songtao Wang, Yan Wang, Yunhai |
| contents | Predicting the next pickup location of individual users is a fundamental problem in intelligent mobility systems, which requires modeling personalized travel behaviors under complex spatiotemporal contexts. Existing methods mainly learn sequential dependencies from raw trajectories, but often fail to capture high-level behavioral semantics and to effectively disentangle long-term habitual preferences from short-term contextual intentions. In this paper, we propose a semantic embedding based dual stream spatiotemporal attention model for next pickup location prediction. Raw trajectories are first transformed into semantically enriched activity sequences to encode users' stay behaviors and movement semantics. A dual stream architecture is then designed to explicitly decouple long-term historical patterns and short-term dynamic intentions, where each stream employs spatiotemporal attention mechanisms to model dependencies at different temporal scales. To integrate heterogeneous contextual information, a context aware dynamic fusion module adaptively balances the contributions of the two streams. Finally, an attention based matching strategy is used to predict the probability distribution over candidate pickup locations. Experiments on real world ride hailing datasets demonstrate that the proposed model consistently outperforms state of the art methods, validating the effectiveness of semantic trajectory abstraction and dual stream spatiotemporal attention for individualized mobility behavior modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding Zhang, Lingyu Xu, Pengfei Ban, Rui Zhang, Zhenchao Liu, Songtao Wang, Yan Wang, Yunhai Information Retrieval Artificial Intelligence Predicting the next pickup location of individual users is a fundamental problem in intelligent mobility systems, which requires modeling personalized travel behaviors under complex spatiotemporal contexts. Existing methods mainly learn sequential dependencies from raw trajectories, but often fail to capture high-level behavioral semantics and to effectively disentangle long-term habitual preferences from short-term contextual intentions. In this paper, we propose a semantic embedding based dual stream spatiotemporal attention model for next pickup location prediction. Raw trajectories are first transformed into semantically enriched activity sequences to encode users' stay behaviors and movement semantics. A dual stream architecture is then designed to explicitly decouple long-term historical patterns and short-term dynamic intentions, where each stream employs spatiotemporal attention mechanisms to model dependencies at different temporal scales. To integrate heterogeneous contextual information, a context aware dynamic fusion module adaptively balances the contributions of the two streams. Finally, an attention based matching strategy is used to predict the probability distribution over candidate pickup locations. Experiments on real world ride hailing datasets demonstrate that the proposed model consistently outperforms state of the art methods, validating the effectiveness of semantic trajectory abstraction and dual stream spatiotemporal attention for individualized mobility behavior modeling. |
| title | Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2509.22661 |